Detection of multiple change-points in multivariate time series

Lavielle, Marc; Teyssière, Gilles

doi:10.1007/s10986-006-0028-9

Cited by 145 publications

(100 citation statements)

References 29 publications

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“…• In the case where K is unknown, many authors (such that Lavielle & Moulines, 2000;Lavielle & Teyssière, 2006;Birgé & Massart, 2007) are proposed different values of penalized parameter for the performance of this method.…”

Section: Pls-methodsmentioning

confidence: 99%

“…It is clearly that the FD-method with parameters optimized is better the PLS-method adaptive (Lavielle & Teyssière, 2006) for the criteria mean integrate square error. In other hand, the FD-method with parameters optimized has less no detection of points that the PLS-method adaptive but the firstly has many false alarms that the secondly.…”

Section: Numerical Conclusionmentioning

confidence: 95%

“…For comparing the both-methods (The filtered derivative with parameters optimized and adaptive method of (Lavielle & Teyssière, 2006), we choose the simulation of the subsection (1.2). For FD-method, the optimal parameters chosen are A opt = 250 and C 1,opt = 0.25.…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

“…To improve the FD-method, two methods are developed: The filtered derivative with pvalue (FDpV) (Bertrand, Fhima, & Guillin, 2011) and the filtered derivative and false discovery rate (FDqV) (Elmi, 2014). For the PLS-method, many authors are proposed (Lavielle & Moulines, 2000;Lavielle & Teyssière, 2006) the choices of penalized parameter for performing the PLS-method. For FD-method, there were no papers which mentioned this.…”

Section: Introductionmentioning

confidence: 99%

“…In other hand, we know the filtered derivative (Basseville & Nikirov, 1993) depends the parameters such that the threshold and the window, we give in order to choose the optimal parameters. We compare the results of Filtered Derivative optimized parameters and the Penalized Square Error methods in particulary the adaptive method of (Lavielle & Teyssière, 2006). …”

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confidence: 99%

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The Parameters Optimization of Filtered Derivative for Change Points Analysis

Elmi¹

2014

IJSP

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Let X = (X 1 , X 2 , . . . , X N ) be a time series. That is a sequence random variable indexed by the time t = (1, 2, . . . , N), we suppose that the parameters of X are piecewise constant. In other words, it exists a subdivision τ = (τ 1 < τ 2 < . . . < τ K ) such that X i is a family of independent and identically distributed (i.i.d) random variables for i ∈ (τ k , τ k+1 ], and k = 0, 1, . . . , K where by convention τ o = 0 and τ K+1 = N. The preceding works such that (Bertrand, 2000) control the probability of false alarms for minimizing the probability of type I error of change point analysis. The novelty in this work is to control the number of false alarms. We give an bound of number of false alarms and the necessary condition for number of no detection. In other hand, we know the filtered derivative (Basseville & Nikirov, 1993) depends the parameters such that the threshold and the window, we give in order to choose the optimal parameters. We compare the results of Filtered Derivative optimized parameters and the Penalized Square Error methods in particulary the adaptive method of (Lavielle & Teyssière, 2006).

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Section: Pls-methodsmentioning

confidence: 99%

Section: Numerical Conclusionmentioning

confidence: 95%

Section: Monte Carlo Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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The Parameters Optimization of Filtered Derivative for Change Points Analysis

Elmi¹

2014

IJSP

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Multivariate Outliers

Barnett¹

2005

Encyclopedia of Biostatistics

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Traditional methods for inference in change point detection often rely on a large number of observed data points and can be inaccurate in non-asymptotic settings. With the rise of mobile health and digital phenotyping studies, where patients are monitored through the use of smartphones or other digital devices, change point detection is needed in non-asymptotic settings where it may be important to identify behavioral changes that occur just days before an adverse event such as relapse or suicide. Furthermore, analytical and computationally efficient means of inference are necessary for the monitoring and online analysis of large-scale digital phenotyping cohorts. We extend the result for asymptotic tail probabilities of the likelihood ratio test to the multivariate change point detection setting, and demonstrate through simulation its inaccuracy when the number of observed data points is not large. We propose a non-asymptotic approach for inference on the likelihood ratio test, and compare the efficiency of this estimated p-value to the popular empirical p-value obtained through simulation of the null distribution. The accuracy and power of this approach relative to competing methods is demonstrated through simulation and through the detection of a change point in the behavior of a patient with schizophrenia in the week prior to relapse.

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Forecasting portfolio returns with skew‐geometric Brownian motions

Bufalo

Liseo

Orlando

2022

Appl Stoch Models Bus & Ind

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The gist of this work is to propose a minimum tracking error portfolio that could be adopted not only as an automated alternative to ETFs but, it could also be potentially used to anticipate market changes in the target index. This goal has been achieved by adopting skew Brownian motion as a general framework.The proposed solution has been declined in two versions: the case in which the constituents (i.e., in our case the subindices) of the objective portfolio are uncorrelated among each other, and the case in which correlation should be taken into account. Our tests, carried out on the S&P 500, as an example of a developed market, and Bovespa, as an example of an emerging market, shows that the proposed solutions replicate the index with a much smaller tracking error than that of the ETFs considered.

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Detection of multiple change-points in multivariate time series

Cited by 145 publications

References 29 publications

The Parameters Optimization of Filtered Derivative for Change Points Analysis

The Parameters Optimization of Filtered Derivative for Change Points Analysis

Multivariate Outliers

Forecasting portfolio returns with skew‐geometric Brownian motions

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